Flow style-aware network for arbitrary style transfer

Zhenshan Hu, Bin Ge, Chenxing Xia, Wenyan Wu, Guangao Zhou, Baotong Wang

Abstract: Highlights•We designed a flow style transfer module (FSTM) based on the flow network. It uses the characteristics of a flow network to transfer style and content features, generating a sample feature containing content and style. It can prevent images from generating repetitive patterns.•We propose a dynamic regulation attention mechanism (DRAM), which can effectively transfer the style information in the sample features to the content features. DRAM is able to utilize the output of the FSTM. Avoiding the direct use of FSTM leads to residual shadows in the generated image.•We introduce a style feature interaction module (SFIM), which can extract the style tensor of the four-layer output of the VGG network, thereby optimizing the final generated image. We use high and low frequency division to reduce SFIM calculation costs.